Thought I’d do a post with links to some tools I use. Some of this is specific to blogging using Squarespace, but some of it could be more generally useful!

A few weeks ago I was at an event where one of the speakers was talking about how transformative generative AI is for start-ups. Not only could you use these new tools to build products, but likely you could also use them to reduce the number of people you need in a start-up. He claimed that it may now be possible to build a billion dollar start-up with only one person!

I adjudicated all Tangled terminal states for both three- and four-vertex complete game board graphs using three different approaches — a numerical Schrodinger Equation solver, a specially tuned simulated annealing approach run on the s=1 Hamiltonian, and a D-Wave Advantage2.4 system using multiple embeddings. I found 100% agreement between all three for all terminal states for both graphs.

If we want to use a processor to solve a problem that is much smaller than the size of the hardware graph, one cool technique we can use is to find multiple non-overlapping ways to fit our problem into the hardware graph. These ‘fits’ are called embeddings. If you can find a bunch, then each time you call the processor, you get all of them solved for you at once. Not only does this give you a pretty big speed up (up to about 200-350x faster for the problems I’ll show you here), but it gives a neat way to average over certain types of noise in a processor.

Evaluating who won a Tangled game requires solving the Schrodinger equation with N qubits, giving O(2ᴺ) scaling in both required memory and compute time.

A software agent is an artificial intelligence software program designed to take actions (ie have agency) in response to the state of their environment.

Let’s look at how to build game-playing agents for any Tangled game board. We can then play against these agents, and agents can play against each other!

The three-vertex Tangled game board I introduced in the previous post is the smallest where one player can win (two vertex games always end in a tie). Let’s take a look at Tangled on a four-vertex six-edge graph. It's still stupidly small, but maybe we can learn something!

In the previous post, I introduced the Tangled game, and we analyzed a two-player two-vertex variant. It wasn’t very interesting, because there was no way for either player to win — every game had to end in a draw! Here we’ll work through the smallest game board where one player can win. This game has three vertices.

In an earlier post, I introduced the idea of constructing games where evaluating terminal states requires solving a problem where a quantum supremacy claim has been made. Here I’ll make this concrete by introducing a game which I call Tangled, which has this property.

I’m going to try to explain what quantum physics really is, why you should care, and how it relates to quantum computers (it’s freaking awesome by the way, even without D&D (Deepak & Drugs), or even AD&D (Advanced Deepak & Drugs)).

The last thing I’d like to talk about before introducing our first target game is a generalization of correlation which I call influence.

The Universe appears to be made out of a small number of patterns, where all the apparent complexity of the world comes from them combining in different ways.

Before I introduce the game I’ll be focusing on, I need to first describe the concept of correlation. You probably already know what correlation means. But let’s review anyway!

Because of the relationship of goal-seeking to intelligence, if we can show certain categories of goals can only be achieved if an agent has access to a quantum computer, then we can define a new sort of intelligence that is categorically different from, and superior to, any intelligence that doesn’t have access to a quantum computer.

The quantum computing field has been around for 25+ years and we still don’t have a single use case where it makes sense to use a QC vs the alternatives. Yes, QCs are amazing testbeds for fundamental science. But they should also be useful! And right now, they … aren’t. At least not in the clearcut way we all want them to be.

Snowdrop’s mission is to take exotic problems where quantum supremacy might exist already, and try to build real-world uses of these where the full performance of the entire application is better than you could reasonably do with any conventional classical computer system.